Method and apparatus for image texture describing

ABSTRACT

A method for retrieving an image texture descriptor for describing texture features of an image, including the steps of (a) filtering input images using predetermined filters having different orientation coefficients, (b) projecting the filtered images onto axes of each predetermined direction to obtain data groups consisting of averages of each directional pixel values, (c) selecting candidate data groups among the data groups by a predetermined classification method, (d) determining a plurality of indicators based on orientation coefficients of the filters used in filtering the candidate data groups, and (e) determining the plurality of indicators as the texture descriptor of the image. The texture descriptors which allow kinds of texture structure present in an image to be perceptually captured can be retrieved.

REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. nonprovisionalapplication Ser. No. 10/434,150, filed May 9, 2003, which claims thebenefit of U.S. nonprovisional application Ser. No.09/497,504, filedFeb. 4, 2000, which claims the benefit of U.S. provisional applicationSer. No. 60/118,740, filed Feb. 5, 1999, the disclosures of which areincorporated herein by reference.

BACKGROUND OF THE INVENTION

[0002] 1. Field of the Invention

[0003] The present invention relates to a method and apparatus forretrieving an image texture descriptor, and more particularly, to animage texture descriptor retrieving method for retrieving a texturedescriptor which is used in searching and browsing an image anddescribes texture characteristics of the image, and an apparatusthereof.

[0004] 2. Description of the Related Art

[0005] Recently, image texture has emerged as important visual featuresfor searching and browsing a large set of similar image patterns. Forexample, a conventional texture descriptor for filtering a texturedescriptor by a Gabor filter extracts a texture descriptor consisting ofcoefficients obtained by Gabor filtering. However, although conventionalimage texture descriptors consist of numerous vectors, it is quitedifficult to visually perceive texture structures from the texturedescriptor.

SUMMARY OF THE INVENTION

[0006] It is an object of the present invention to provide a method forretrieving an image texture descriptor which can perceptually capturethe texture structures present in an image.

[0007] It is another object of the present invention to provide acomputer readable storage medium having a computer program storedtherein, the program being arranged such that a computer executes theimage texture descriptor retrieving method.

[0008] It is still another object of the present invention to provide animage texture descriptor retrieving apparatus which performs the imagetexture descriptor retrieving method.

[0009] To achieve the above object, there is provided a method forretrieving an image texture descriptor for describing texture featuresof an image, including the steps of (a) filtering input images usingpredetermined filters having different orientation coefficients, (b)projecting the filtered images onto axes of each predetermined directionto obtain data groups consisting of averages of each directional pixelvalues, (c) selecting candidate data groups among the data groups by apredetermined classification method, (d) determining a plurality ofindicators based on orientation coefficients of the filters used infiltering the candidate data groups, and (e) determining the pluralityof indicators as the texture descriptor of the image.

[0010] The step (a) may further include the step of (a-1) filteringinput images using predetermined filters having different scalecoefficients, and the step (d) further comprises the step of (d-1)determining a plurality of indicators based on scale coefficients of thefilters used in filtering the candidate data groups.

[0011] The image texture descriptor retrieving method may furtherinclude the step of determining another indicator based on the presenceof data groups filtered by filters having scale coefficients ororientation coefficients which are close to or identical with the scalecoefficients or orientation coefficients of the filters used infiltering the selected candidate data groups.

[0012] The image texture descriptor retrieving method may furtherinclude the step of calculating the mean and variance of pixels withrespect to the filtered images, and obtaining a predetermined vectorusing the calculated mean and variance.

[0013] According to another aspect of the present invention, there isprovide a method for retrieving an image texture descriptor fordescribing texture features of an image, includes the steps of (a)filtering input images using predetermined filters having differentscale coefficients, (b) projecting the filtered images onto axes of eachpredetermined direction to obtain data groups consisting of averages ofeach directional pixel values, (c) determining a plurality of indicatorsbased on scale coefficients of the filters used in filtering data groupsselected among the data groups by a predetermined selection method, (d)determining the plurality of indicators as the texture descriptor of theimage.

[0014] According to still another aspect of the present invention, thereis provided method for retrieving an image texture descriptor fordescribing texture features of an image, comprising the steps of (a)filtering input images using predetermined filters having differentorientation coefficients and different scale coefficients, (b)projecting the filtered images onto horizontal and vertical axes toobtain horizontal-axis projection graphs and vertical-axis projectiongraphs, (c) calculating normalized auto-correlation values for eachgraph, (d) obtaining local maximums and local minimum for eachnormalized auto-correlation value, at which the calculated normalizedauto-correlation values forms a local peak and a local valley at apredetermined section, (e) defining the average of the local maximumsand the average the local minimums as contrast, (f) selecting graphs inwhich the ratio of the standard deviation to the average of the localmaximums is less than or equal to a predetermined threshold as firstcandidate graphs, (g) determining the type of the second candidategraphs according to the number of graphs filtered by the filters havingscale coefficients or orientation coefficients which are close to oridentical with the scale coefficients or orientation coefficients of thefilters used in filtering the selected second candidate graphs, (h)counting the numbers of graphs belonging to the respective types ofsecond candidate graphs and determining predetermined weights of eachtype of second candidate graphs, (i) calculating the sum of products ofthe counted numbers of graphs and the determined weights to determinethe calculation result value as a first indicator constituting a texturedescriptor, (j) determining the orientation coefficients and scalecoefficients of the second candidate graphs having the biggest contrastas second through fifth indicators, and (k) determining indicatorsincluding the first indicator and the second through fifth indicators asthe texture descriptors of the corresponding image.

[0015] The image texture descriptor retrieving method may furtherinclude the step of calculating the mean and variance of pixels withrespect to the filtered images, and obtaining a predetermined vectorusing the calculated mean and variance, wherein the step (k) includesthe step of determining indicators including the first indicator, thesecond through fifth indicators and the predetermined vector as thetexture descriptors of the corresponding image.

[0016] The normalized auto-correlation, denoted by NAC(k), is preferablycalculated by the following formula:${{NAC}(k)} = \frac{\sum\limits_{m = k}^{N - 1}\quad {{P\left( {m - k} \right)}{P(m)}}}{\sqrt{\sum\limits_{m = k}^{N - 1}\quad {{P^{2}\left( {m - k} \right)}{\sum\limits_{m = k}^{N - 1}\quad {P^{2}(m)}}}}}$

[0017] wherein N is a predetermined positive integer, an input imageconsists of N×N pixels, a pixel position is represented by i, where i isa number from 1 to N, the projection graphs expressed by pixels of thepixel position i is represented by P(i) and k is a number from 1 to N.

[0018] The contrast is determined as:${contrast} = {{\frac{1}{M}{\sum\limits_{i = 1}^{M}\quad {{P\_ magn}(i)}}} - {\frac{1}{L}{\sum\limits_{i = 1}^{L}\quad {{V\_ magn}(i)}}}}$

[0019] wherein P_magn (i) and V_magn (i) are the local maximums andlocal minimums determined in the step (d).

[0020] In the step (f), the graphs satisfying the following formula areselected as first candidate graphs: $\frac{S}{d} \leq \alpha$

[0021] wherein d and S are the average and standard deviation of thelocal maximums and a is a predetermined threshold.

[0022] The step (g) includes the sub-steps of (g-1) if there are one ormore graphs having scale or orientation coefficients identical withthose of a pertinent candidate graph and one or more graphs having scaleor orientation coefficients close to those of the pertinent candidategraph, classifying the pertinent candidate graph as a first type graph,(g-2) if there are one or more graphs having scale or orientationcoefficients identical with those of a pertinent candidate graph butthere is no graph having scale or orientation coefficients close tothose of the pertinent candidate graph, classifying the pertinentcandidate graph as a second type graph, and (g-3) if there is no graphhaving scale or orientation coefficients identical with or close tothose of a pertinent candidate graph, classifying the pertinentcandidate graph as a third type graph.

[0023] The step (h) includes the step of counting the number of graphsbelonging to each of the first through third types of graphs anddetermining predetermined weights for each of the types of graphs.

[0024] After the step of (f), there may be further included the step ofapplying a predetermined clustering algorithm to the first candidategraphs to select second candidate graphs.

[0025] The predetermined clustering algorithm is preferably modifiedagglomerative clustering.

[0026] Preferably, in the step k), the orientation coefficient of agraph having the biggest contrast, among the horizontal-axis projectiongraphs, is determined as a second indicator; the orientation coefficientof a graph having the biggest contrast, among the vertical-axisprojection graphs, is determined as a second indicator; the scalecoefficient of a graph having the biggest contrast, among thehorizontal-axis projection graphs, is determined as a fourth indicator;and the scale coefficient of a graph having the biggest contrast, amongthe vertical-axis projection graphs, is determined as a fifth indicator.

[0027] The step (j) may include the step of determining indicatorsincluding the first indicator, the second through fifth indicators andthe predetermined vector as the texture descriptors of the correspondingimage.

[0028] The predetermined filters preferably include Gabor filters.

[0029] To achieve the second object of the present invention, there isprovided a computer readable medium having program codes executable by acomputer to perform a method for an image texture descriptor fordescribing texture features of an image, the method including the stepsof (a) filtering input images using predetermined filters havingdifferent orientation coefficients and different scale coefficients, (b)projecting the filtered images onto horizontal and vertical axes toobtain horizontal-axis projection graphs and vertical-axis projectiongraphs, (c) calculating normalized auto-correlation values for eachgraph, (d) obtaining local maximums and local minimums for each ofnormalized auto-correlation values, at which the calculated normalizedauto-correlation value forms a local peak and a local valley at apredetermined section, (e) defining the average of the local maximumsand the average the local minimums as contrast, (f) selecting graphs inwhich the ratio of the standard deviation to the average of the localmaximums is less than or equal to a predetermined threshold as firstcandidate graphs, (g) determining the type of the second candidategraphs according to the number of graphs filtered by the filters havingscale coefficients or orientation coefficients which are close to oridentical with the scale coefficients or orientation coefficients of thefilters used in filtering the selected second candidate graphs, (h)counting the numbers of graphs belonging to the respective types ofsecond candidate graphs and determining predetermined weights of eachtype of second candidate graph, (i) calculating the sum of products ofthe counted numbers of graphs and the determined weights to determinethe calculation result value as a first indicator constituting a texturedescriptor, (j) determining the orientation coefficients and scalecoefficients of the second candidate graphs having the biggest contrastas second through fifth indicators, and (k) determining indicatorsincluding the first indicator and the second through fifth indicators asthe texture descriptors of the corresponding image.

[0030] To achieve the third object of the present invention, there isprovided an apparatus method for retrieving an image texture descriptorfor describing texture features of an image, the apparatus includingfiltering mean for filtering input images using predetermined filtershaving different orientation coefficients, projecting means forprojecting the filtered images onto axes of each predetermined directionto obtain data groups consisting of averages of each directional pixelvalues, classifying means for selecting candidate data groups among thedata groups by a predetermined classification method, first indicatordetermining means for determining another indicator based on the numberof graphs filtered by filters having scale coefficients or orientationcoefficients which are close to or identical with the scale coefficientsor orientation coefficients of the filters used in filtering theselected candidate graph, and second indicator determining means fordetermining a plurality of indicators based on scale coefficients andorientation coefficients of the filters used in filtering the determinedcandidate graphs.

[0031] Alternatively, there is provided an apparatus for retrieving animage texture descriptor for describing texture features of an image,the apparatus including a filtering unit for filtering input imagesusing predetermined filters having different orientation coefficientsand different scale coefficients, an image mean/variance calculatingunit for calculating the mean and variance of pixels with respect toeach of the filtered images, and obtaining a predetermined vector usingthe calculated mean and variance, a projecting unit for projecting thefiltered images onto horizontal and vertical axes to obtainhorizontal-axis projection graphs and vertical-axis projection graphs, acalculating unit for calculating a normalized auto-correlation value foreach graph, a peak detecting/analyzing unit for detecting local maximumsand local minimums for each auto-correlation value, at which thecalculated normalized auto-correlation values forms a local peak and alocal valley at a predetermined section, a mean/variance calculatingunit for calculating the average of the local maximums and the averagethe local minimums, a first candidate graph selecting/storing unit forselecting the graphs satisfying the requirement that the ratio of thestandard deviation to the average of the local maximums be less than orequal to a predetermined threshold, as first candidate graphs, a secondcandidate graph selecting/storing unit for applying a predeterminedclustering algorithm to the first candidate graphs to select the same assecond candidate graphs, a classifying unit for counting the number ofgraphs belonging to each of the respective types of the second candidategraphs, outputting data signals indicative of the number of graphs ofeach type, determining predetermined weights of the graphs belonging tothe respective types and outputting data signals indicative of weightsto be applied to each type, a first indicator determining unit forcalculating the sum of the products of the data representing the numberof graphs belonging to each type, and the data representing the weightsto be applied to each type, determining and outputting the calculationresult as a first indicator constituting a texture descriptor, acontrast calculating unit for calculating the contrast according toformula (2) using the averages output from the mean/variance calculatingunit and outputting a signal indicating that the calculated contrast isbiggest, a second candidate graph selecting/storing unit for outputtingthe candidate graphs having the biggest contrast among the secondcandidate graphs stored therein in response to the signal indicatingthat the calculated contrast is biggest, a second-to-fifth indicatordetermining unit for determining the orientation coefficient of a graphhaving the biggest contrast, among the horizontal-axis projectiongraphs; the orientation coefficient of a graph having the biggestcontrast, among the vertical-axis projection graphs, as a secondindicator; the scale coefficient of a graph having the biggest contrast,among the horizontal-axis projection graphs, as a fourth indicator; andthe scale coefficient of a graph having the biggest contrast, among thevertical-axis projection graphs, as a fifth indicator, and a texturedescriptor output unit for combining the first indicator, the secondthrough fifth indicators and the predetermined vector and outputting thecombination result as the texture descriptors of the corresponding image

BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The above objects and advantages of the present invention willbecome more apparent by describing in detail preferred embodimentsthereof with reference to the attached drawings in which.

[0033]FIGS. 1A and 1B are flow diagrams showing an image texturedescriptor retrieving method according to the present invention;

[0034]FIG. 2 is a block diagram of an image texture descriptorretrieving apparatus according to the present invention; and

[0035]FIG. 3 shows perceptual browsing components (PBCs) extracted fromBrodatz texture images by simulation based on the image texturedescriptor retrieving method according to the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0036] Hereinafter, embodiments of the present invention will bedescribed in detail with reference to the accompanying drawings.

[0037] Referring to FIG. 1A showing an image texture descriptorretrieving method according to the present invention, assuming that N isa predetermined positive integer, an input image consisting of N×Npixels, for example, 128×128 pixels, is filtered using a Gabor filter(step 100). The Gabor filter is constituted by filters having differentorientation coefficients and different scale coefficients. Assuming thatC1 and C2 are predetermined positive integers, the input image isfiltered by filters having C1 kinds of orientation coefficients and C2kinds of scale coefficients, and the filters output C1×C2 kinds offiltered images.

[0038] Next, the mean and variance of pixels are calculated for each ofthe C1×C2 kinds of filtered images, and then a vector Z is obtainedusing the mean and variance (step 102).

[0039] Then, the filtered images are projected onto x- and y-axes toobtain x-projection graphs and y-projection graphs (step 104). Thenormalized auto-correlation (NAC) value for each graph P(i) (i is anumber from 1 to N) denoted by NAC(k), is calculated by the followingformula (1): $\begin{matrix}{{{NAC}(k)} = \frac{\sum\limits_{m = k}^{N - 1}\quad {{P\left( {m - k} \right)}{P(m)}}}{\sqrt{\sum\limits_{m = k}^{N - 1}\quad {{P^{2}\left( {m - k} \right)}{\sum\limits_{m = k}^{N - 1}\quad {P^{2}(m)}}}}}} & (1)\end{matrix}$

[0040] wherein a pixel position is represented by i, the projectiongraphs expressed by pixels of the pixel position i are represented byP(i) and k is a number from 1 to N (N is a positive integer.).

[0041] Next, local maximums P_magn (i) and local minimums of V_magn (i),at which as the calculated NAC(k) forms a peak and a valley locally at apredetermined section, are obtained (step 108).

[0042] Now, contrast is defined as the following formula (2):$\begin{matrix}{{contrast} = {{\frac{1}{M}{\sum\limits_{i = 1}^{M}\quad {{P\_ magn}(i)}}} - {\frac{1}{L}{\sum\limits_{i = 1}^{L}\quad {{V\_ magn}(i)}}}}} & (2)\end{matrix}$

[0043] (step 110).

[0044] Also, the graphs satisfying the following formula (3) areselected as first candidate graphs (step 112): $\begin{matrix}{\frac{S}{d} \leq \alpha} & (3)\end{matrix}$

[0045] wherein d and S are the average and standard deviation of thelocal maximums P_magni (i) and α is a predetermined threshold.

[0046] Referring to FIG. 1B, modified agglomerative clustering isapplied to the first candidate graphs to select second candidate graphs(step 114). A modified agglomerative clustering algorithm is anappropriately modified algorithm of agglomerative clustering disclosedby R. O. Duda and P. E. Hart in “Pattern Classification and SceneAnalysis, John Wiley and Sons, New York, 1973,” which will now bedescribed briefly. First, in N graphs P₁, . . . , P_(N), let the meanand standard deviation of distances between peaks be d_(i) and S_(i),and each graph have a two-dimensional vector corresponding to (d_(i),S_(i)). Now, Pi is clustered using the two-dimensional vectorcorresponding to (d_(i), S_(i)) as follows. Assuming that the desirednumber of clusters is M_(c), with respect to initial number of clustersN, each cluster C_(i) can be expressed such that C₁={P₁}, C₂={P₂}, . . ., C_(N)={P_(N)}. If the number of clusters is smaller than M_(c),clustering is stopped. Next, two clusters C_(i) and C_(j) which are mostdistant from each other are obtained. If the distance between C_(i) andC_(j) is greater than a predetermined threshold, clustering is stopped.Otherwise, C_(i) and C_(j) are merged to remove one of the two clusters.This procedure is repeatedly performed until the number of clustersreaches a predetermined number. Then, among the clustered clusters, thecluster having the most graphs is selected and graphs in the selectedcluster are selected as candidate graphs.

[0047] Now, the second candidate graphs are classified into three types(step 116). The classification is performed according to the number ofgraphs filtered by a filter having scale or orientation coefficientswhich are close to or identical with those of a filter used forfiltering the second candidate graphs. Hereinafter, for the convenience'sake of explanation, the graphs filtered by a filter having a certainscale coefficient or a constant orientation coefficient will be referredto as certain-scale-coefficient graphs orcertain-orientation-coefficient graphs.

[0048] In more detail, first, in the case where there are one or moregraphs having scale or orientation coefficients identical with those ofa pertinent candidate graph and one or more graphs having scale ororientation coefficients close to those of the pertinent candidategraph, the pertinent candidate graph is classified as a C1 type graph.Second, in the case where there are one or more graphs having scale ororientation coefficients identical with those of a pertinent candidategraph but there is no graph having scale or orientation coefficientsclose to those of the pertinent candidate graph, the pertinent candidategraph is classified as a C2 type graph. Third, in the case where thereis no graph having scale or orientation coefficients identical with orclose to those of a pertinent candidate graph, the pertinent candidategraph is classified as a C3 type graph. Then, the numbers of graphsbelonging to each of the C1, C2 and C3 types are counted to be denotedby N₁, N₂ and N₃, respectively, and the respective weights of the graphsbelonging to each of the C1, C2 and C3 types are counted to be denotedby W₁, W₂ and W₃, respectively, which will be described below.

[0049] Now, using the determined numbers N₁, N₂ and N₃, and the weightsW₁, W₂ and W₃, the following calculation is performed: $\begin{matrix}{M = {\sum\limits_{i = 1}^{3}\quad {N_{i} \times W_{i}}}} & (4)\end{matrix}$

[0050] wherein the result M is determined as a first indicator V₁constituting a texture descriptor (step 118).

[0051] With respect to the second candidate graphs, the orientationcoefficients and scale coefficients of graphs that have the biggestcontrast are determined as second through fifth indicators (step 120).In more detail, the orientation coefficient of a graph having thebiggest contrast, among the x-projection graphs, is determined as asecond indicator V₂. Also, the orientation coefficient of a graph havingthe biggest contrast, among the y-projection graphs, is determined as athird indicator V₃. The scale coefficient of a graph having the biggestcontrast, among the x-projection graphs, is determined as a fourthindicator V₄. Also, the scale coefficient of a graph having the biggestcontrast, among the y-projection graphs, is determined as a fifthindicator V₅.

[0052] Using the first indicator V₁ determined in the step 118, thesecond through fifth indicators V₂, V₃, V₄ and V₅, and the vector Zdetermined in the step 102, the texture descriptor, that is, the texturefeature vector, is set to {[V₁, V₂, V₃, V₄, V₅], Z} (step 122).

[0053] A large first indicator V₁ indicates a high level ofstructuredness of the texture of an image. It has been experimentallyconfirmed that the first indicator V₁ represents quite well thestructuredness of the texture of an image. The second and thirdindicators V₂ and V₃ represent two quantized orientations in which thestructuredness is captured most. The fourth and fifth indicators V₄ andV₅ represent two quantized scales in which the structuredness iscaptured most.

[0054] The texture descriptor is used as an index of an image inbrowsing or searching-retrieval applications. Especially, the imagetexture descriptor retrieved by the image texture descriptor retrievingmethod according to the present invention is suitably used in checkermarks in which browsing patterns are regular, or structure orientedbrowsing, i.e., or embroidery patterns. Thus, in searching structurallysimilar patterns, image searching which is more adaptable toeye-perception is allowed by applying the image texture descriptorretrieving method according to the present invention to the applicationsbased on the structured oriented browsing. Therefore, among indicatorsconstituting texture descriptors retrieved by the image texturedescriptor retrieving method according to the present invention, thefirst through fifth indicators V₁, V₂, V₃, V₄ and V₅ can be referred toas perceptual browsing components (PBCs).

[0055] Also, with respect to each filtered image, the mean and varianceof pixel values are calculated. The vector Z obtained by using the meanand variance can be referred to as similarity retrieval components(SRCs).

[0056] In other words, in the image texture descriptor retrieving methodaccording to the present invention; the texture descriptor allows kindsof texture structures present in an image to be perceptually captured.

[0057] It has been described that a first indicator V₁ which is a quitea good indicator of the structuredness of the texture of an image,second and third indicators V₂ and V₃ representing two quantizedorientations in which the structuredness is captured most, fourth andfifth indicators V₄ and V₅ representing two quantized scales in whichthe structuredness is captured most, are used as the texture descriptorsof the image. However, the above-described embodiment is used in adescriptive sense only and not for the purpose of limitation. A singleindicator that is most suitable to the characteristics of an image andarbitrarily selected plural indicators, can also be used as the texturedescriptor(s) of the image. Therefore, the above-described embodiment isnot intended as a restriction on the scope of the invention.

[0058] Also, the image texture descriptor retrieving method isprogrammable by a computer program. Codes and code segments constitutingthe computer program can be easily derived by a computer programmer inthe art. Also, the program is stored in computer readable media and isreadable and executable by the computer, thereby embodying the imagetexture descriptor retrieving method. The media include magneticrecording media, optical recording media, carrier wave media, and thelike.

[0059] Also, the image texture descriptor retrieving method can beembodied by an image texture descriptor retrieving apparatus. FIG. 2 isa block diagram of an image texture descriptor retrieving apparatusaccording to the present invention. Referring to FIG. 2, the imagetexture descriptor retrieving apparatus includes a Gabor filer 200, animage mean/variance calculating unit 202, an x-axis projector 204, ay-axis projector 205, an NAC calculating unit 206 and a peakdetecting/analyzing unit 208. Also, the image texture descriptorretrieving apparatus includes a mean/variance calculating unit 210, afirst candidate graph selecting/storing unit 212, a second candidategraph selecting/storing unit 214, a classifying unit 216, a firstindicator determining unit 218, a contrast calculating unit 220, asecond-to-fifth indicator determining unit 222 and a texture descriptoroutput unit 224.

[0060] In the operation of the image texture descriptor retrievingapparatus, assuming that N is a predetermined positive integer, theGabor filter 200 filters an input image consisting of N×N pixels, forexample, 128×128 pixels using filters (not shown) having differentorientation coefficients and different scale coefficients, and outputsfiltered images (image_filtered). Assuming that C1 and C2 arepredetermined positive integers, the input image is filtered by filtershaving C1 kinds of orientation coefficients and C2 kinds of scalecoefficients, and the filters output C1×C2 kinds of filtered images.

[0061] The image mean/variance calculating unit 202 calculates the meanand variance of pixels for each of the C1×C2 kinds of filtered images,to then obtain a vector Z using the mean and variance and outputs theobtained vector Z.

[0062] The x-axis projector 204 and the y-axis projector 205 project thefiltered images onto x- and y-axes to obtain x-projection graphs andy-projection graphs. In other words, suppose a pixel position isrepresented by i (i is a number from 1 to N), the x-axis projector 204and the y-axis projector 205 output the projection graphs P(i) expressedby pixels of the pixel position i (i−1, . . . , N).

[0063] The NAC calculating unit 206 calculates the normalizedauto-correlation (NAC) value for each graph P(i), denoted by NAC(k),using the formula (1).

[0064] The peak detecting/analyzing unit 208 detects local maximumsP_magn (i) and local minimums of V_magn (i), at which the calculatedNAC(k) forms a local peak and a local valley at a predetermined section.

[0065] The mean/variance calculating unit 210 calculates the mean d andstandard deviation S of the local maximums P_magn (i) and outputs thesame. The first candidate graph selecting/storing unit 212 receives themean d and standard deviation S, selects the graphs satisfying theformula (3) as first candidate graphs (1st_CAND) and stores the selectedfirst candidate graphs, in which a is a predetermined threshold.

[0066] The second candidate graph selecting/storing unit 214 appliesmodified agglomerative clustering to the first candidate graphs toselect the same as second candidate graphs (2nd_CAND).

[0067] The classifying unit 216, as described with reference to FIG. 1B,counts the numbers of graphs belonging to each of the C1, C2 and C3types to denote the same by N₁, N₂ and N₃, respectively, with respect tothe second candidate graphs, and outputs data signals N_(i) indicativeof the number of graphs of each type. Also, the classifying unit 216determines predetermined weights of the graphs belonging to each of theC1, C2 and C3 types to then denote the same by W₁, W₂ and W₃,respectively, and outputs data signals W_(i) indicative of weights to beapplied to each type.

[0068] The first indicator determining unit 218 calculates M asrepresented by the formula (4) using the determined numbers N₁, N₂ andN₃, and the weights W₁, W₂ and W₃, and determines and outputs thecalculation result as a first indicator V₁ constituting a texturedescriptor.

[0069] The contrast calculating unit 220 calculates the contrast by theformula (2) and outputs a signal Cont_max indicating that the calculatedcontrast is biggest.

[0070] The second candidate graph selecting/storing unit 214 outputs thecandidate graphs having the biggest contrast among the second candidategraphs stored therein to the second-to-fifth indicator determining unit222.

[0071] The second-to-fifth indicator determining unit 222 determines theorientation coefficients and scale coefficients of graphs that have thebiggest contrast as second through fifth indicators. In other words, theorientation coefficient of a graph having the biggest contrast, amongthe x-projection graphs, is determined as a second indicator V₂. Also,the orientation coefficient of a graph having the biggest contrast,among the y-projection graphs, is determined as a second indicator V₃.The scale coefficient of a graph having the biggest contrast, among thex-projection graphs, is determined as a fourth indicator V₄. Also, thescale coefficient of a graph having the biggest contrast, among they-projection graphs, is determined as a fifth indicator V₅.

[0072] The texture descriptor output unit 224 sets and outputs thetexture descriptor, that is, the texture feature vector, as {[V₁, V₂,V₃, V₄, V₅], Z}, using the first indicator V₁ output from the firstindicator determining unit 218, the second through fifth indicators V₂,V₃, V₄ and V₅ output from the second-to-fifth indicator determining unit222 and the vector Z output from the image mean/variance calculatingunit 202.

[0073]FIG. 3 shows perceptual browsing components (PBCs) extracted fromBrodatz texture images by simulation based on the image texturedescriptor retrieving method according to the present invention.

[0074] As described above, according to the image texture descriptorretrieving method of the present invention, texture descriptors whichallow kinds of texture structure present in an image to be perceptuallycaptured can be retrieved.

What is claimed is:
 1. A method for retrieving an image texturedescriptor for describing texture features of an image, comprising thesteps of: (a) filtering input images using predetermined filters havingdifferent orientation coefficients; (b) projecting the filtered imagesonto axes of each predetermined direction to obtain data groupsconsisting of averages of each directional pixel values; (c) selectingcandidate data groups among the data groups by a predeterminedclassification method; (d) determining a plurality of indicators basedon orientation coefficients of the filters used in filtering thecandidate data groups, and (e) determining the plurality of indicatorsas the texture descriptor of the image.
 2. The image texture descriptorretrieving method according to claim 1, wherein the step (a) furthercomprises the step of (a-1) filtering input images using predeterminedfilters having different scale coefficients, and the step (d) furthercomprises the step of (d-1) determining a plurality of indicators basedon scale coefficients of the filters used in filtering the candidatedata groups.
 3. The image texture descriptor retrieving method accordingto claim 2, further comprising the step of determining another indicatorbased on the presence of data groups filtered by filters having scalecoefficients or orientation coefficients a which are close to oridentical with the scale coefficients or orientation coefficients of thefilters used in filtering the selected candidate data groups.
 4. Theimage texture descriptor retrieving method according to claim 3, furthercomprising the step of calculating the mean and variance of pixels withrespect to each of the filtered images, and obtaining a predeterminedvector using the calculated mean and variance.
 5. The image texturedescriptor retrieving method according to claim 2, further comprisingthe step of calculating the mean and variance of pixels with respect tothe filtered images, and obtaining a predetermined vector using the acalculated mean and variance.
 6. The image texture descriptor retrievingmethod according to claim 1, further comprising the step of determininganother indicator based on the presence of graphs filtered by filtershaving scale coefficients or orientation coefficients which are close toor identical with the scale coefficients or orientation coefficients ofthe filters used in filtering the selected candidate data groups.
 7. Theimage texture descriptor retrieving method according to claim 6, furthercomprising the step of calculating the mean and variance of pixels withrespect to each of the filtered images, and obtaining a predeterminedvector using the calculated mean and variance.
 8. The image texturedescriptor retrieving method according to claim 1, further comprisingthe step of calculating the mean and variance of pixels with respect toeach of the filtered images, and obtaining a predetermined vector usingthe calculated mean and variance.
 9. A method for retrieving an imagetexture descriptor for describing texture features of an image,comprising the steps of: (a) filtering input images using predeterminedfilters having different scale coefficients; (b) projecting the filteredimages onto axes of each predetermined direction to obtain data groupsconsisting of averages of each directional pixel values; (c) determininga plurality of indicators based on scale coefficients of the filtersused in filtering data groups selected among the data groups by apredetermined selection method; (d) determining the plurality ofindicators as the texture descriptor of the image.
 10. The image texturedescriptor retrieving method according to claim 9, further comprisingthe step of calculating the mean and variance of pixels with respect tothe filtered images, and obtaining a predetermined vector using thecalculated mean and variance.
 11. A method for retrieving an imagetexture descriptor for describing texture features of an image,comprising the steps of: (a) filtering input images using predeterminedfilters having different orientation coefficients and different scalecoefficients; (b) projecting the filtered images onto axes of eachpredetermined direction to obtain graphs consisting of averages of eachdirectional pixel values; (c) selecting candidate graphs among thegraphs obtained in the step (b) by a predetermined classificationmethod; (d) determining another indicator based on the presence ofgraphs filtered by filters having scale coefficients or orientationcoefficients which are close to or identical with the scale coefficientsor orientation coefficients of the filters used in filtering theselected candidate graphs; (e) determining a plurality of indicatorsbased on scale coefficients or orientation coefficients of the filtersused in filtering the determined candidate graphs; and (f) determiningthe indicator determined in the step (d) and the plurality of indicatorsdetermined in the step (e) as the texture descriptor of the image. 12.The image texture descriptor retrieving method according to claim 11,further comprising the step of calculating the mean and variance ofpixels with respect to the filtered images, and obtaining apredetermined vector using the calculated mean and variance.
 13. Amethod for retrieving an image texture descriptor for describing texturefeatures of an image: comprising the steps of: (a) filtering inputimages using predetermined filters having different orientationcoefficients and different scale coefficients; (b) projecting thefiltered images onto horizontal and vertical axes to obtainhorizontal-axis projection graphs and vertical-axis projection graphs;(c) calculating normalized auto-correlation values for each graph; (d)obtaining local maximums and local minimum for each normalizedauto-correlation value, at which the calculated normalizedauto-correlation values forms a local peak and a local valley at apredetermined section; (e) defining the average of the local maximumsand the average the local minimums as contrast; (f) selecting graphs inwhich the ratio of the standard deviation to the average of the localmaximums is less than or equal to a predetermined threshold as firstcandidate graphs; (g) determining the type of the second candidategraphs according to the number of graphs filtered by the filters havingscale coefficients or orientation coefficients which are close to oridentical with the scale coefficients or orientation coefficients of thefilters used in filtering the selected second candidate graphs; (h)counting the numbers of graphs belonging to the respective types ofsecond candidate graphs and determining predetermined weights of eachtype of second candidate graphs; (i) calculating the sum of products ofthe counted numbers of graphs and the determined weights to determinethe calculation result value as a first indicator constituting a texturedescriptor; (j) determining the orientation coefficients and scalecoefficients of the second candidate graphs having the biggest contrastas second through fifth indicators; and (k) determining indicatorsincluding the first indicator and the second through fifth indicators asthe texture descriptors of the corresponding image.
 14. The imagetexture descriptor retrieving method according to claim 13, furthercomprising the step of calculating the mean and variance of pixels withrespect to the filtered images, and obtaining a predetermined vectorusing the calculated mean and variance, wherein the step (k) includesthe step of determining indicators including the first indicator, thesecond through fifth indicators and the predetermined vector as thetexture descriptors of the corresponding image.
 15. The image texturedescriptor retrieving method according to claim 1, wherein thenormalized auto-correlation, denoted by NAC(k), is calculated by thefollowing formula:${{NAC}(k)} = \frac{\sum\limits_{m = k}^{N - 1}\quad {{P\left( {m - k} \right)}{P(m)}}}{\sqrt{\sum\limits_{m = k}^{N - 1}\quad {{P^{2}\left( {m - k} \right)}{\sum\limits_{m = k}^{N - 1}\quad {P^{2}(m)}}}}}$

wherein N is a predetermined positive integer, an input image consistsof N×N pixels, a pixel position is represented by i, where i is a numberfrom 1 to N, the projection graphs expressed by pixels of the pixelposition i is represented by P(i) and k is a number from 1 to N.
 16. Theimage texture descriptor retrieving method according to claim 12.wherein the contrast is determined as:${contrast} = {{\frac{1}{M}{\sum\limits_{i = 1}^{M}\quad {{P\_ magn}(i)}}} - {\frac{1}{L}{\sum\limits_{i = 1}^{L}\quad {{V\_ magn}(i)}}}}$

wherein P_magn (i) and V_magn (i) are the local maximums and localminimums determined in the step (d).
 17. The image texture descriptorretrieving method according to claim 13, wherein in the step (f), thegraphs satisfying the following formula are selected as first candidategraphs: $\frac{S}{d} \leq \alpha$

wherein d and S are the average and standard deviation of the localmaximums and α is a predetermined threshold.
 18. The image texturedescriptor retrieving method according to claim 13, wherein the step (g)comprises the sub-steps of: (g-1) if there are one or more graphs havingscale or orientation coefficients identical with those of a pertinentcandidate graph and one or more graphs having scale or orientationcoefficients close to those of the pertinent candidate graph,classifying the pertinent candidate graph as a first type graph; (g-2)if there are one or more graphs having scale or orientation coefficientsidentical with those of a pertinent candidate graph but there is nograph having scale or orientation coefficients close to those of thepertinent candidate graph, classifying the pertinent candidate graph asa second type graph; and (g-3) if there is no graph having scale ororientation coefficients identical with or close to those of a pertinentcandidate graph, classifying the pertinent candidate graph as a thirdtype graph.
 19. The image texture descriptor retrieving method accordingto claim 13, wherein the step (h) includes the step of counting thenumber of graphs belonging to each of the first through third types ofgraphs and determining predetermined weights for each of the types ofgraphs.
 20. The image texture descriptor retrieving method according toclaim 13, after the step of (f), further comprising the step of applyinga predetermined clustering algorithm to the first candidate graphs toselect second candidate graphs.
 21. The image texture descriptorretrieving method according to claim 20, wherein the predeterminedclustering algorithm is modified agglomerative clustering
 22. The imagetexture descriptor retrieving method according to claim 13, wherein inthe step (j), the orientation coefficient of a graph having the biggestcontrast, among the horizontal-axis projection graphs, is determined asa second indicator; the orientation coefficient of a graph having thebiggest contrast, among the vertical-axis projection graphs, isdetermined as a second indicator; the scale coefficient of a graphhaving the biggest contrast, among the horizontal-axis projectiongraphs, is determined as a fourth indicator; and the scale coefficientof a graph having the biggest contrast, among the vertical-axisprojection graphs, is determined as a fifth indicator.
 23. The imagetexture descriptor retrieving method according to claim 13, wherein thestep (j) includes the step of determining indicators including the firstindicator, the second through fifth indicators and the predeterminedvector as the texture descriptors of the corresponding image.
 24. Theimage texture descriptor retrieving method according to claim 13,wherein the predetermined filters include Gabor filters.
 25. The imagetexture descriptor retrieving method according to claim 14, wherein thepredetermined filters include Gabor filters.
 26. The image texturedescriptor retrieving method according to claim 15, wherein thepredetermined filters include Gabor filters.
 27. The image texturedescriptor retrieving method according to claim 16, wherein thepredetermined filters include Gabor filters.
 28. The image texturedescriptor retrieving method according to claim 17, wherein thepredetermined filters include Gabor filters.
 29. The image texturedescriptor retrieving method according to claim 18, wherein thepredetermined filters include Gabor filters.
 30. The image texturedescriptor retrieving method according to claim 19, wherein thepredetermined filters include Gabor filters.
 31. A computer readablemedium having program codes executable by a computer to perform a methodfor an image texture descriptor for describing texture features of animage, the method comprising the steps of: (a) filtering input imagesusing predetermined filters having different orientation coefficientsand different scale coefficients; (b) projecting the filtered imagesonto horizontal and vertical axes to obtain horizontal-axis projectiongraphs and vertical-axis projection graphs; (c) calculating normalizedauto-correlation values for each graph; (d) obtaining local maximums andlocal minimums for each of normalized auto-correlation values, at whichthe calculated normalized auto-correlation value forms a local peak anda local valley at a predetermined section; (e) defining the average ofthe local maximums and the average the local minimums as contrast; (f)selecting graphs in which the ratio of the standard deviation to theaverage of the local maximums is less than or equal to a predeterminedthreshold as first candidate graphs; (g) determining the type of thesecond candidate graphs according to the number of graphs filtered bythe filters having scale coefficients or orientation coefficients whichare close to or identical with the scale coefficients or orientationcoefficients of the filters used in filtering the selected secondcandidate graphs; (h) counting the numbers of graphs belonging to therespective types of second candidate graphs and determiningpredetermined weights of each type of second candidate graph; (i)calculating the sum of products of the counted numbers of graphs and thedetermined weights to determine the calculation result value as a firstindicator constituting a texture descriptor; (j) determining theorientation coefficients and scale coefficients of the second candidategraphs having the biggest contrast as second through fifth indicators;and (k) determining indicators including the first indicator and thesecond through fifth indicators as the texture descriptors of thecorresponding image.
 32. The computer readable medium according to claim31, wherein the image texture descriptor retrieving method furthercomprises the step of calculating the mean and variance of pixels withrespect to the filtered images, and obtaining a predetermined vectorusing the calculated mean and variance, and wherein the step (k)includes the step of determining indicators including the firstindicator, the second through fifth indicators and the predeterminedvector as the texture descriptors of the corresponding image.
 33. Anapparatus method for retrieving an image texture descriptor fordescribing texture features of an image, comprising: filtering mean forfiltering input images using predetermined filters having differentorientation coefficients; projecting means for projecting the filteredimages onto axes of each predetermined direction to obtain data groupsconsisting of averages of each directional pixel values; classifyingmeans for selecting candidate data groups among the data groups by apredetermined classification method; first indicator determining meansfor determining another indicator based on the number of graphs filteredby filters having scale coefficients or orientation coefficients whichare close to or identical with the scale coefficients or orientationcoefficients of the filters used in filtering the selected candidategraph; and second indicator determining means for determining aplurality of indicators is - based on scale coefficients and orientationcoefficients of the filters used in filtering the determined candidategraphs.
 34. The image texture descriptor retrieving apparatus accordingto claim 33, further comprising mean/variance calculating means forcalculating the mean and variance of pixels with respect to each of thefiltered images, and obtaining a predetermined vector using thecalculated mean and variance.
 35. An apparatus for retrieving an imagetexture descriptor for describing texture features of an image,comprising: a filtering unit for filtering input images usingpredetermined filters having different orientation coefficients anddifferent scale coefficients; an image mean/variance calculating unitfor calculating the mean and variance of pixels with respect to each ofthe filtered images, and obtaining a predetermined vector using thecalculated mean and variance; a projecting unit for projecting thefiltered images onto horizontal and vertical axes to obtainhorizontal-axis projection graphs and vertical-axis projection graphs; acalculating unit for calculating a normalized auto-correlation value foreach graph; a peak detecting/analyzing unit for detecting local maximumsand local minimums for each auto-correlation value, at which thecalculated normalized auto-correlation values forms a local peak and alocal valley at a predetermined section; a mean/variance calculatingunit for calculating the average of the local maximums and the averagethe local minimums; a first candidate graph selecting/storing unit forselecting the graphs satisfying the requirement that the ratio of thestandard deviation to the average of the local maximums be less than orequal to a predetermined threshold, as first candidate graphs; a secondcandidate graph selecting/storing unit for applying a predeterminedclustering algorithm to the first candidate graphs to select the same assecond candidate graphs; a classifying unit for counting the number ofgraphs belonging to each of the respective types of the second candidategraphs, outputting data signals indicative of the number of graphs ofeach type, determining predetermined weights of the graphs belonging tothe respective types and outputting data signals indicative of weightsto be applied to each type; a first indicator determining unit forcalculating the sum of the products of the data representing the numberof graphs belonging to each type, and the data representing the weightsto be applied to each type, determining and outputting the calculationresult as a first indicator constituting a texture descriptor; acontrast calculating unit for calculating the contrast according toformula (2) using the averages output from the mean/variance calculatingunit and outputting a signal indicating that the calculated contrast isbiggest; a second candidate graph selecting/storing unit for outputtingthe candidate graphs having the biggest contrast among the secondcandidate graphs stored therein in response to the signal indicatingthat the calculated contrast is biggest; a second-to-fifth indicatordetermining unit for determining the orientation coefficient of a graphhaving the biggest contrast, among the horizontal-axis projectiongraphs; the orientation coefficient of a graph having the biggestcontrast, among the vertical-axis projection graphs, as a secondindicator; the scale coefficient of a graph having the biggest contrast,among the horizontal-axis projection graphs, as a fourth indicator; andthe scale coefficient of a graph having the biggest contrast, among thevertical-axis projection graphs, as a fifth indicator; and a texturedescriptor output unit for combining the first indicator, the secondthrough fifth indicators and the predetermined vector and outputting thecombination result as the texture descriptors of the correspondingimage.